We can apply background matting to the videos.
Launch the demo from the mmpose root directory:
python tools/webcam/run_webcam.py --config tools/webcam/configs/background/background.py
Hotkey | Function |
---|---|
b | Toggle the background matting effect on/off. |
h | Show help information. |
m | Show the monitoring information. |
q | Exit. |
Note that the demo will automatically save the output video into a file record.mp4
.
Users can choose detection models from the MMDetection Model Zoo. Just set the model_config
and model_checkpoint
in the detector node accordingly, and the model will be automatically downloaded and loaded.
Note that in order to perform background matting, the model should be able to produce segmentation masks.
# 'DetectorNode':
# This node performs object detection from the frame image using an
# MMDetection model.
dict(
type='DetectorNode',
name='Detector',
model_config='demo/mmdetection_cfg/mask_rcnn_r50_fpn_2x_coco.py',
model_checkpoint='https://download.openmmlab.com/'
'mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/'
'mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392'
'__segm_mAP-0.354_20200505_003907-3e542a40.pth',
input_buffer='_input_', # `_input_` is a runner-reserved buffer
output_buffer='det_result'),
If you don't have GPU and CUDA in your device, the demo can run with only CPU by setting device='cpu'
in all model nodes. For example:
dict(
type='DetectorNode',
name='Detector',
model_config='demo/mmdetection_cfg/mask_rcnn_r50_fpn_2x_coco.py',
model_checkpoint='https://download.openmmlab.com/'
'mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/'
'mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392'
'__segm_mAP-0.354_20200505_003907-3e542a40.pth',
device='cpu',
input_buffer='_input_', # `_input_` is a runner-reserved buffer
output_buffer='det_result'),
You can launch the webcam runner with a debug config:
python tools/webcam/run_webcam.py --config tools/webcam/configs/examples/test_camera.py